location_on United States

Share

Badri Narayanan Gopalakrishnan

Specialises In

Dr. Badri Narayanan Gopalakrishnan is an economist, affiliated with University of Washington Seattle. He co-founded Infinite-Sum Modeling Inc., with offices in Canada, USA, India and China. His broad expertise lies in the analysis for business strategy and public policy, employing a variety of quantitative models. Recently, apart from his 17+ years of applied economic research experience in trade, energy/environment and development issues for several international and national organizations including the UN, World Bank, IMF, etc., he has been working on several other issues such as business economics of new technologies, including AI, blockchain, internet of things, cloud, 3-d printing, robotics, drones, etc., advising several start-ups in these sectors. He has written 75+ research papers and presented his work in 24 countries, with thousands of researchers citing his research.

schedule 1 month ago

Sold Out!

45 Mins

Case Study

Intermediate

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.